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. 2022 May 24:10:885624.
doi: 10.3389/fpubh.2022.885624. eCollection 2022.

A Visualized Dynamic Prediction Model for Overall Survival in Elderly Patients With Pancreatic Cancer for Smart Medical Services

Jiang Zhong  1 , XingShu Liao  1 , Shuang Peng  2 , Junyi Cao  3 , Yue Liu  4 , Chunyang Liu  5 , Ju Qiu  5 , Xiaoyan Guan  4 , Yang Zhang  6 , Xiaozhu Liu  7 , Shengxian Peng  5
Affiliations

A Visualized Dynamic Prediction Model for Overall Survival in Elderly Patients With Pancreatic Cancer for Smart Medical Services

Jiang Zhong et al. Front Public Health. .

Abstract

Background: Pancreatic cancer (PC) is a highly malignant tumor of the digestive system. The number of elderly patients with PC is increasing, and older age is related to a worse prognosis. Accurate prognostication is crucial in treatment decisions made for people diagnosed with PC. However, an accurate predictive model for the prognosis of these patients is still lacking. We aimed to construct nomograms for predicting the overall survival (OS) of elderly patients with PC.

Methods: Patients with PC, older than 65 years old from 2010 to 2015 in the Surveillance, Epidemiology, and End Results database, were selected and randomly divided into training cohort (n = 4,586) and validation cohort (n = 1,966). Data of patients in 2016-2018 (n = 1,761) were used for external validation. Univariable and forward stepwise multivariable Cox analysis was used to determine the independent prognostic factors. We used significant variables in the training set to construct nomograms predicting prognosis. The performance of the models was evaluated for their discrimination and calibration power based on the concordance index (C-index), calibration curve, and the decision curve analysis (DCA).

Results: Age, insurance, grade, surgery, radiation, chemotherapy, T, N, and American Joint Commission on Cancer were independent predictors for OS and thus were included in our nomogram. In the training cohort and validation cohort, the C-indices of our nomogram were 0.725 (95%CI: 0.715-0.735) and 0.711 (95%CI: 0.695-0.727), respectively. The 1-, 3-, and 5-year areas under receiver operating characteristic curves showed similar results. The calibration curves showed a high consensus between observations and predictions. In the external validation cohort, C-index (0.797, 95%CI: 0.778-0.816) and calibration curves also revealed high consistency between observations and predictions. The nomogram-related DCA curves showed better clinical utility compared to tumor-node-metastasis staging. In addition, we have developed an online prediction tool for OS.

Conclusions: A web-based prediction model for OS in elderly patients with PC was constructed and validated, which may be useful for prognostic assessment, treatment strategy selection, and follow-up management of these patients.

Keywords: SEER database; elderly patients; nomogram; online application; pancreatic cancer.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
The flowchart of including and dividing patients. Github: https://github.com/xiaoyang11223/pancreatic-cancer.git.
Figure 2
Figure 2
Nomogram for 1-, 3-, and 5-year OS of elderly patients with PC.
Figure 3
Figure 3
Calibration curves of the nomogram. (A) For 1-, 3-, and 5-year OS in training cohort; (B) for 1-, 3-, and 5-year OS in validation cohort; and (C) for 1-, 3-, and 5-year OS in validation cohort.
Figure 4
Figure 4
The ROC for OS of 1-, 3- and 5-year of training cohort (A), validation cohort (B), and external validation cohort (C).
Figure 5
Figure 5
Decision curves of the nomogram predicting OS in validation cohort (A) and external validation cohort (B). The y-axis represents the net benefit, and the x-axis represents the threshold probability. The purple line indicates that no patients have died, and the blue line indicates that all patients have died. When the threshold probability is between 20 and 60%, the net benefit of the model exceeds all deaths or no deaths.
Figure 6
Figure 6
Kaplan–Meier curves of OS for patients in the low- and high-risk groups in all cohorts (A), training cohort (B), validation cohort (C), and external validation cohort (D).
See this image and copyright information in PMC

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